Universal model for UV/Vis spectroscopy of gold nanoparticles
Machine learning models have limited interoperability in analytical chemistry; mostly.
Experiments always vary between labs. This can be enough to disturb machine learning models trained on small data.
- Different equipment used
- Different sample preparation
- Different calibration methods
- Differences in executions
The list goes on and on.
Here is a beautiful example of a βuniversal machine learning modelβ Gleason et al. (2024) for UV/Vis spectroscopy of gold nanoparticles. A method that almost always works!
Gold Nanorod shape distribution prediction
The model is trained on simulated UV/Vis spectra of gold nanorods with different aspect ratios, and it can predict the shape distribution from the spectrum.
The method is called AuNR-SMA and outlined in the paper βAutomated Gold Nanorod Spectral Morphology Analysis Pipelineβ. It is a computational method, but not machine learning.
The raw material is a bag of simulated spectra for gold nanorods (AuNRs) with varying aspect ratios and dimensions. The authors assume that the particles dimensions follow a Gaussian distribution. The observed spectrum is then reconstructed sum of spectra of individual particles. There is no learning involved, just physics and math.
β Interoperability Comparison: AuNR-SMA vs Traditional Methods
Feature | AuNR-SMA | Traditional TEM | Empirical UV-Vis Methods |
---|---|---|---|
π¬ Equipment Required | β Any UV-Vis-NIR spectrophotometer | β Expensive TEM facility | β οΈ Specific calibrated instrument |
π Cross-Lab Transfer | β Physics-based = Universal | β Direct imaging | β Requires recalibration |
β±οΈ Analysis Time | β <5 minutes | β Hours + sample prep | β Minutes |
π° Cost per Sample | β <$1 | β $50-200 | β <$1 |
π Information Obtained | β Full size distributions | β Individual particles | β οΈ Only average values |
π€ Automation Ready | β Fully automated | β οΈ Limited | β οΈ Instrument-specific |
π©βπ¬ Expertise Needed | β Minimal training | β Specialized training | β οΈ Method development |
π Throughput | β 100s/day | β 10s/day | β 100s/day |
π― What Makes AuNR-SMA Universally Transferable
Enabler
The paper mentions explores three applications.
- The authors automate the analysis of one-pot seedless high throughput AuNR synthesis.
- Then they train machine learning models to predict AuNR synthesis outcomes
- They use spectra from the literature to infer population level data that was not reported
Any type of lab can benefit from this method.
Lab Type | How AuNR-SMA Helps |
---|---|
π Industrial Scale-Up | Monitor batch-to-batch consistency without TEM delays |
π¬ Academic Research | Publish complete size distributions, not just TEM samples |
π₯ Biomedical Applications | Rapid QC for therapeutic nanoparticles |
π€ Collaborative Projects | Same analysis method across all partner labs |
π Literature Review | Extract quantitative data from published spectra |